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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (12): 45-54    DOI: 10.11925/infotech.2096-3467.2020.0959
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Analyzing Sentiments and Dissemination of Misinformation on Public Health Emergency
Zhang Yipeng,Ma Jingdong()
School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
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Abstract  

[Objective] This paper examines mis-information on public health emergency (i.e., the COVID-19 epidemic), aiming to reveal the public’s sentiments on mis-information and the latter’s dissemination patterns. [Methods] We retrieved our data from Sina Weibo and categorized the relevant microblog posts using machine learning techniques. Then, we extracted the post topics with LDA model and decided the emotional polarity of comments using dictionary method. Finally, we used T-test to compare the number of comments, shares and likes received by mis-information posts with different sentiments. [Results] We found that 46.28% of the retrieved blogs had mis-information. 59.32% of the posts with mis-information and 54.49% of the posts with accurate information yielded negative emotion among readers. On average, the misinformation posts with negative sentiments received more comments, shares and likes than those with positive sentiments (2.26, 2.68 and 3.29). [Limitations] We only examined COVID-19 related posts and did not investigate the dissemination of accurate information. [Conclusions] Public health emergency generates much mis-information. The sentiments of misinformation readers are more negative than those of normal information. Weibo posts with misinformation and negative sentiments attract more online participation.

Key wordsPublic Health Emergency      Misinformation      Sentiment Analysis      Information Dissemination     
Received: 29 September 2020      Published: 29 October 2020
ZTFLH:  TP391  
Corresponding Authors: Ma Jingdong     E-mail: jdma@hust.edu.cn

Cite this article:

Zhang Yipeng,Ma Jingdong. Analyzing Sentiments and Dissemination of Misinformation on Public Health Emergency. Data Analysis and Knowledge Discovery, 2020, 4(12): 45-54.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.0959     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I12/45

Research Approach
预测结果 误导信息 非误导信息 合计
阳性(+) 59 2 61
阴性(-) 3 72 75
合计 62 74 136
Test Results of Random Forest and SVM Model
Examples of Misinformation About the COVID-19 Epidemic
Perplexity-Topic Line Chart
主题

词语1 词语2 词语3 词语4 词语5 词语6 词语7 词语8 词语9
主题1 取消 病毒 疾病 蝙蝠 河南 重磅 非典 野生 隔离
11.06% 7.49% 6.27% 4.80% 4.61% 1.40% 0.76% 0.75% 0.70%
主题2 疑似 组织 世卫 病例 确诊 传人 症状 全国 新增
5.34% 4.88% 4.35% 4.29% 3.80% 3.75% 3.39% 2.87% 2.57%
主题3 指挥部 治疗 死亡 政策 应急 严重 病房 武汉市 崩溃
6.41% 5.51% 4.30% 3.53% 3.52% 3.20% 3.05% 2.67% 2.27%
主题4 政府 行程 聚集 首例 航班 提醒 支援 减少 交通
8.94% 6.53% 6.40% 5.58% 5.46% 5.25% 2.95% 2.78% 2.46%
主题5 工作 时间 抗疫 口罩 措施 疫情 影响 医疗 呼吸机
15.76% 4.59% 4.57% 4.33% 3.33% 2.85% 2.33% 2.18% 1.89%
主题6 开学 学校 学生 延期 集中 领导组 结束 工作 提前
5.65% 5.41% 4.71% 4.21% 4.04% 3.75% 3.36% 3.16% 3.03%
主题7 加油 国家 累计 英雄 卫健委 确诊 严格 接受 感谢
9.24% 7.82% 7.41% 7.04% 6.32% 5.32% 3.32% 2.91% 2.18%
主题8 发现 院士 关注 网络 钟南山 公主 直播 明天 采访
8.30% 6.62% 5.44% 5.00% 4.76% 4.11% 4.01% 3.40% 1.36%
主题9 美国 世界 核酸 报道 军运会 报告 特朗普 疫情 表示
7.37% 6.85% 6.00% 4.51% 3.29% 2.92% 2.72% 2.39% 2.38%
COVID-19 Epidemic Related Weibo Topics-Word Distribution Probability
Distribution Probability of Sentiment Polarity of Weibo Comments on Different Topics
类别 评论数(平均值±标准差) t p
正面情感 负面情感
病毒起源 29.47±5.22 30.66±16.02 -1.06 0.29
病例通报 20.45±11.94 23.91±12.87 -2.29 0.02
疫情冲击和影响 28.89±13.94 31.37±14.78 -1.39 0.16
交通状况 21.26±13.35 28.52±15.49 -4.18 0.00
物资状况 24.83±9.95 22.09±11.53 2.44 0.02
学习工作状态 18.39±10.64 20.97±10.72 -1.89 0.06
鼓舞士气 22.61±12.99 21.09±14.28 1.10 0.27
关键意见领袖 29.02±12.00 32.15±11.90 -2.69 0.01
国际疫情 25.12±11.22 24.89±13.69 0.19 0.85
合计 24.51±12.18 26.77±14.33 -4.84 0.00
t-Test of the Number of Weibo Comments with Positive Emotions and Negative Emotions
类别 转发数(平均值±标准差) t p
正面情感 负面情感
病毒起源 23.85±10.99 28.51±13.87 3.20 0.00
病例通报 25.68±12.36 21.73±13.33 -2.53 0.01
疫情冲击和影响 25.68±13.75 21.73±15.42 1.69 0.09
交通状况 19.08±11.21 24.01±12.12 3.53 0.00
物资状况 24.86±13.66 30.57±11.65 4.19 0.00
学习工作状态 20.75±8.38 19.10±11.77 -1.30 0.20
鼓舞士气 21.59±8.39 20.72±13.23 -0.73 0.47
关键意见领袖 27.43±11.41 31.50±16.91 2.92 0.00
国际疫情 22.91±10.35 25.98±14.99 2.44 0.02
合计 23.43±11.23 26.11±14.47 5.94 0.00
t-Test of the Number of Weibo Reposts with Positive Emotions and Negative Emotions
类别 点赞数(平均值±标准差) t p
正面情感 负面情感
病毒起源 48.75±37.41 58.62±39.10 2.05 0.04
病例通报 51.56±29.25 46.44±29.37 -1.46 0.15
疫情冲击和影响 58.45±35.77 59.63±36.06 0.27 0.79
交通状况 48.34±39.17 55.93±39.33 1.64 0.10
物资状况 56.52±43.65 55.38±41.77 -0.25 0.80
学习工作状态 48.14±34.55 47.78±30.38 -0.09 0.93
鼓舞士气 46.84±33.76 44.72±32.63 -0.62 0.54
关键意见领袖 59.54±46.09 63.43±41.92 0.91 0.36
国际疫情 55.25±36.34 66.41±39.13 3.05 0.00
合计 52.77±38.30 56.06±37.74 2.43 0.02
t-test of the Number of Weibo Like with Positive Emotions and Negative Emotions
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